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Author |
Diego Alejandro Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Camera Egomotion Estimation in the ADAS Context |
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Conference Article |
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Year |
2010 |
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13th International IEEE Annual Conference on Intelligent Transportation Systems |
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1415–1420 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Camera-based Advanced Driver Assistance Systems (ADAS) have concentrated many research efforts in the last decades. Proposals based on monocular cameras require the knowledge of the camera pose with respect to the environment, in order to reach an efficient and robust performance. A common assumption in such systems is considering the road as planar, and the camera pose with respect to it as approximately known. However, in real situations, the camera pose varies along time due to the vehicle movement, the road slope, and irregularities on the road surface. Thus, the changes in the camera position and orientation (i.e., the egomotion) are critical information that must be estimated at every frame to avoid poor performances. This work focuses on egomotion estimation from a monocular camera under the ADAS context. We review and compare egomotion methods with simulated and real ADAS-like sequences. Basing on the results of our experiments, we show which of the considered nonlinear and linear algorithms have the best performance in this domain. |
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Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ITSC |
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ADAS |
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ADAS @ adas @ CPL2010 |
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1425 |
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Author |
Javad Zolfaghari Bengar; Abel Gonzalez-Garcia; Gabriel Villalonga; Bogdan Raducanu; Hamed H. Aghdam; Mikhail Mozerov; Antonio Lopez; Joost Van de Weijer |
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Title |
Temporal Coherence for Active Learning in Videos |
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Conference Article |
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Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
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914-923 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets. |
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Seul; Corea; October 2019 |
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ICCVW |
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LAMP; ADAS; 600.124; 602.200; 600.118; 600.120; 600.141 |
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Admin @ si @ ZGV2019 |
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3294 |
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Felipe Codevilla; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
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Title |
On Offline Evaluation of Vision-based Driving Models |
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Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
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Volume |
11219 |
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Pages |
246-262 |
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Keywords |
Autonomous driving; deep learning |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Autonomous driving models should ideally be evaluated by deploying
them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and
suitable offline metrics. |
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Munich; September 2018 |
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ECCV |
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ADAS; 600.124; 600.118 |
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no |
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Admin @ si @ CLK2018 |
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3162 |
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Author |
German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
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Title |
Vision-based Offline-Online Perception Paradigm for Autonomous Driving |
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Conference Article |
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Year |
2015 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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231 - 238 |
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Keywords |
Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community. |
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Hawaii; January 2015 |
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WACV |
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ADAS; 600.076 |
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ADAS @ adas @ RRG2015 |
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2499 |
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Author |
Jiaolong Xu; Peng Wang; Heng Yang; Antonio Lopez |
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Title |
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving |
Type |
Conference Article |
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Year |
2019 |
Publication |
IEEE International Conference on Robotics and Automation |
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2379-2384 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet-and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB. |
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Montreal; Canada; May 2019 |
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ICRA |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ XWY2018 |
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3182 |
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Author |
Gemma Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo |
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Title |
2D-to-3D Facial Expression Transfer |
Type |
Conference Article |
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Year |
2018 |
Publication |
24th International Conference on Pattern Recognition |
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2008 - 2013 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an
input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular
mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic
equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops. |
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ICPR |
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ADAS; 600.086; 600.130; 600.118 |
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no |
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Admin @ si @ RLM2018 |
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3232 |
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Author |
Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen |
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Title |
Combining Holistic and Part-based Deep Representations for Computational Painting Categorization |
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Conference Article |
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2016 |
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6th International Conference on Multimedia Retrieval |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization.We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.4% and 3.8% respectively on artist and style classification. |
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New York; USA; June 2016 |
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ICMR |
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LAMP; 600.068; 600.079;ADAS |
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no |
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Admin @ si @ RKW2016 |
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2763 |
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Author |
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate |
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Title |
Evaluation of the Capabilities of Confidence Measures for Assessing Optical Flow Quality |
Type |
Conference Article |
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2013 |
Publication |
ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars |
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624-631 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Assessing Optical Flow (OF) quality is essential for its further use in reliable decision support systems. The absence of ground truth in such situations leads to the computation of OF Confidence Measures (CM) obtained from either input or output data. A fair comparison across the capabilities of the different CM for bounding OF error is required in order to choose the best OF-CM pair for discarding points where OF computation is not reliable. This paper presents a statistical probabilistic framework for assessing the quality of a given CM. Our quality measure is given in terms of the percentage of pixels whose OF error bound can not be determined by CM values. We also provide statistical tools for the computation of CM values that ensures a given accuracy of the flow field. |
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Sydney; Australia; December 2013 |
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CVTT:E2M |
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IAM; ADAS; 600.044; 600.057; 601.145 |
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Admin @ si @ MGH2013b |
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2351 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates |
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Conference Article |
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2013 |
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20th International Conference On Neural Information Processing |
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8228 |
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III |
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368-375 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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Daegu; Korea; November 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-42050-4 |
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ICONIP |
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ADAS |
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Admin @ si @ DFD2013 |
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2439 |
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Author |
David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras |
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Title |
Visual Registration Method For A Low Cost Robot: Computer Vision Systems |
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Conference Article |
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2009 |
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7th International Conference on Computer Vision Systems |
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5815 |
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204–214 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance. |
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Belgica |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04666-7 |
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ICVS |
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ADAS |
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no |
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Admin @ si @ ATR2009b |
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1247 |
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